A foundation for evaluating the surgical artificial intelligence literature

• Published in: European journal of surgical oncology p. 108014
• Main Authors: Hashimoto, Daniel A., Sambasastry, Sai Koushik, Singh, Vivek, Kurada, Sruthi, Altieri, Maria, Yoshida, Takuto, Madani, Amin, Jogan, Matjaz
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 10.02.2024
• Subjects: Algorithms; Artificial intelligence; Computer vision; Data science; Deep learning; Machine learning; Deep learning; Data science; Computer vision; Algorithms; Artificial intelligence; Machine learning
• ISSN: 0748-7983; 1532-2157
• DOI: 10.1016/j.ejso.2024.108014

With increasing growth in applications of artificial intelligence (AI) in surgery, it has become essential for surgeons to gain a foundation of knowledge to critically appraise the scientific literature, commercial claims regarding products, and regulatory and legal frameworks that govern the development and use of AI. This guide offers surgeons a framework with which to evaluate manuscripts that incorporate the use of AI. It provides a glossary of common terms, an overview of prerequisite knowledge to maximize understanding of methodology, and recommendations on how to carefully consider each element of a manuscript to assess the quality of the data on which an algorithm was trained, the appropriateness of the methodological approach, the potential for reproducibility of the experiment, and the applicability to surgical practice, including considerations on generalizability and scalability.